The present invention relates generally to the field of audio based analytics, and more particularly to directly analyzing audio signals recorded at an event and correlating the audio signals with additional sources of information about the event.
Audio mining is a technique by which the content of an audio signal can be automatically analyzed and searched. A commonly used method for audio mining is automatic speech recognition, where the analysis tries to identify any speech within the audio. The audio will typically be processed by an automatic speech recognition system in order to identify word or phoneme units that are likely to occur in the spoken content. This information may either be used immediately in pre-defined searches for keywords or phrases (a real-time “word spotting” system), or the output of the speech recognizer may be stored in an index file. One or more audio mining index files can then be loaded at a later date in order to run searches for keywords or phrases.
Audio mining systems using speech recognition are often divided into two groups: those that use Large Vocabulary Continuous Speech Recognizers (LVCSR) and those that use phonetic recognition. LVCSR needs to have hundreds of thousands of words to match the audio against. The output however is a stream of words, making it richer to work with. Since the complete semantic context is in the index, it is possible to find and focus on business issues very rapidly. Phonetic recognition uses the sounds of speech, specifically the physical properties of speech sounds or signs, their physiological production, acoustic properties, auditory perception, and neurophysiological status to recognize speech sounds and the role of the auditory system.
Audio analytics is the process of analyzing audio recordings to gather information, bring structure to speaker's interactions or expose information buried in speaker's interactions. Audio analytics often includes elements of automatic speech recognition, where the identities of spoken words or phrases are determined. One use of speech analytics applications is to spot spoken keywords or phrases, either as alerts on live audio or as a post-processing step on recorded speech. Other uses include categorization of speech, for example in the contact center environment, to identify calls from unsatisfied customers.